Low-coverage whole-genome sequencing (lcWGS) followed by imputation is emerging as a cost-effective method for generating a substantial number of single nucleotide polymorphism (SNP) in aquatic species with highly heterozygous and complex genomes. This study represents the first systematic investigation into the application of low-coverage whole-genome sequencing (lcWGS) combined with imputation for genotyping in Pacific abalone (Haliotis discus hannai) without a reference panel. We utilized 1059 Pacific abalone individuals sequenced at an average depth of 7.86×, as well as 16 individuals sequenced at 20×, as sample materials. To assess the genotype imputation accuracy for lcWGS without a reference panel, we simulated data with varying sequencing depths (0.5–4×) and examined the effects of sample size, chromosome length, and minor allele frequency (MAF) using BaseVar and STITCH strategies. Results showed that STITCH achieved high accuracy when the sample size exceeded 400, with a genotype correlation (R²) of 0.98 ± 0.002 and genotype concordance (GC) of 0.99 ± 0.001. Imputation accuracy plateaued when the sample size exceeded 400 and sequencing depth surpassed 1×. Chromosome length had minimal effects, with all three chromosomes achieving an accuracy of approximately 0.98. However, the accuracy for rare MAF (<0.05) was lower, falling below 0.99. A second imputation with Beagle significantly increased SNP detection by 3.9–8.3 folds for a sequencing depth of 0.5–4×, apparently without sacrificing accuracy. To our knowledge, this is the first study of lcWGS analysis conducted in abalone. The findings demonstrate that lcWGS with imputation can achieve high accuracy with moderate sample sizes (n ≥ 400) in Pacific abalone, offering a cost-effective approach for genotyping in aquaculture species.

Low-density SNP panels are widely used to reduce genotyping costs in genomic selection (GS) in aquaculture, but sparse marker density can constrain prediction accuracy, particularly in species with rapid linkage disequilibrium decay. In this study, we evaluated a locally enriched multi-SNP (mSNP) strategy derived from a conventional 1K SNP panel in a family-based breeding population of Pacific white shrimp (Penaeus vannamei). Targeted sequencing was used to recover multiple nearby variants surrounding each marker locus, and conventional SNP and mSNP panels were compared for genomic features, imputation performance, and genomic prediction accuracy for body weight. The mSNP panel exhibited higher polymorphism and broader gene-region coverage than the corresponding SNP panel. The original mSNP panel improved prediction accuracy by 11.6% over the original 1K SNP panel. This advantage was maintained or slightly improved when mSNPs were restricted to on-target variants within ±300 bp of the target markers. A locus-matched comparison further showed that enriched mSNPs consistently outperformed conventional SNPs derived from the same target loci, with a 12.0% improvement at the full 961-locus panel. Although imputation improved the 1K SNP panel, the original mSNP panel already outperformed the imputed SNP panel and reached 0.509 after imputation. These findings demonstrate that locally enriched mSNP markers provide an effective and practical strategy for improving low-density GS in aquaculture species with rapid LD decay.

Harvest weight uniformity is a critical economic trait in the production of Pacific white shrimp (Penaeus vannamei). Social interactions among individuals can significantly influence both uniformity and productivity in aquaculture. To improve harvest weight uniformity through selective breeding, it is essential to accurately partition the genetic component of social effects, known as an indirect genetic effect (IGE), from purely environmental factors. Since IGEs cannot be estimated when all individuals are kept in a single group, a specialized experimental design, such as the grouping design with three families per group (3FAM), is required. With this experimental design, the shrimp population is divided into multiple groups (cages), each containing three families. Individuals from each family are then evenly subdivided and placed in three cages, thereby enabling the estimation of both direct and social genetic effects. Additionally, integrating genomic information instead of relying solely on pedigree data improves the accuracy of genetic relatedness among individuals, leading to more precise genetic evaluation. This study employed a 3FAM experimental design involving 40 families (36 individuals per family) to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. The genotypes of all tested individuals obtained using the 55K SNP panel were incorporated into a hierarchical generalized linear model to predict direct genetic effects and indirect genetic effects (IGE) separately. The results revealed that the heritability of harvest weight uniformity was low (0.005 to 0.017). However, the genetic coefficient of variation (0.340 to 0.528) indicates that using the residual variance in harvest weight as a selection criterion for improving uniformity is feasible. Incorporating IGE into the model increased heritability estimates for uniformity by 150% to 240% and genetic coefficient of variation for uniformity by 32.11% to 55.29%, compared to the model without IGE. Moreover, the genetic correlation between harvest weight and its uniformity shifted from a strongly negative value (−0.862 to −0.683) to a weakly positive value (0.203 to 0.117), suggesting an improvement in the genetic relationship between the traits and better separation of genetic and environmental effects. The inclusion of genomic data enhanced the prediction ability of single-step best linear unbiased prediction for both harvest weight and uniformity by 6.35% and 10.53%, respectively, compared to the pedigree-based best linear unbiased prediction. These findings highlight the importance of incorporating IGE and utilizing genomic selection methods to enhance selection accuracy for obtaining harvest weight uniformity. This approach provides a theoretical foundation for guiding uniformity improvements in shrimp breeding programs and offers potential applications in other food production systems.

The Pacific white shrimp (Penaeus vannamei) exhibits pronounced sexual dimorphism in growth, making early and accurate sex identification crucial for targeted monitoring of sexual development and in-depth research into monosex aquaculture. However, morphological sex identification is unreliable at early developmental stages, and single-marker molecular assays are prone to diagnostic failures caused by genotyping errors or missing data. In this study, we developed a multi-SNP framework for sex prediction by integrating genome-wide association studies (GWAS) with Random Forest (RF) modeling. Analyzing 1244 individuals with a 55K SNP array, we identified 18 genome-wide significant SNPs and 10 candidate genes, including SRRM1 and SPSB3, that may be associated with sexual differentiation. Subsequent multi-marker comparative analyses demonstrated that although the top-performing single marker yielded high accuracy, simulated genotyping errors and missing data caused significant drops in accuracy and elevated no-call rates, whereas multi-SNP panels exhibited substantially improved robustness. A stable performance plateau was reached when expanding the panel to 5 SNPs. The model achieved 100% classification accuracy in the two mixed-sex independent populations, ZJ and HN, and correctly identified all females in the all-female MK cohort. Overall, this study provides a robust multi-SNP tool for sex identification in P. vannamei and highlights the practical utility of machine learning in aquaculture.

Litopenaeus vannamei, the most widely farmed crustacean, relies on family-based selection where accurate pedigree information is essential. Although SNP-based tools offer high-accuracy pedigree assignment, adoption in commercial breeding remains limited. In this study, we developed a commercially viable 1K SNP panel with 1125 markers. Markers were selected from a 55K SNP dataset comprising 2330 individuals. We established a practical pedigree reconstruction workflow and implemented the panel in a field breeding population. The population included a selection group where families were reared separately and a test group where individuals were communally reared. We introduced anchor individuals from the selection group to enable pedigree linkage. All 1818 individuals from 72 families were accurately assigned. Family reconstruction achieved 100% consistency with known records, even when parents were partially missing. Heritability estimates for harvest weight ranged from 0.32 to 0.36 using pedigree-based BLUP (PBLUP), genomic BLUP (GBLUP), and single-step genomic BLUP (ssGBLUP). The ssGBLUP model, using a 0.15 to 0.85 weighting of G and A, achieved 6.67% and 19.40% higher accuracy than PBLUP and GBLUP. The panel also supported population structure analysis and diversity monitoring, demonstrating its value for genomic evaluation in commercial L. vannamei breeding.